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Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets.
Instructors: Murtaza Haider
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Beginner level
Just basic computer literacy and willingness to self-learn online. No prior knowledge of computer science or programming languages required.
(3,002 reviews)
Recommended experience
Beginner level
Just basic computer literacy and willingness to self-learn online. No prior knowledge of computer science or programming languages required.
Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression
Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables
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Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.
The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.
Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects.
No prior knowledge of computer science or programming languages required.
This program is ACE® recommended—when you complete, you can earn up to 8 college credits.
Applied Learning Project
All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:
Extracting and graphing financial data with the Pandas data analysis Python library
Generating visualizations and conducting statistical tests to provide insight on housing trends using census data
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools
Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Create and manage source code for data science using Git repositories and GitHub.
Learn Python - the most popular programming language and for Data Science and Software Development.
Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
Access and web scrape data using APIs and Python libraries like Beautiful Soup.
Play the role of a Data Scientist / Data Analyst working on a real project.
Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
Apply Python fundamentals, Python data structures, and working with data in Python.
Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.
Write Python code to conduct various statistical tests including a T test, an ANOVA, and regression analysis.
Interpret the results of your statistical analysis after conducting hypothesis testing.
Calculate descriptive statistics and visualization by writing Python code.
Create a final project that demonstrates your understanding of various statistical test using Python and evaluate your peer's projects.
Analyze data within a database using SQL and Python.
Create a relational database and work with multiple tables using DDL commands.
Construct basic to intermediate level SQL queries using DML commands.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
When you complete this Specialization, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹
When you complete this Specialization, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹
Illinois Tech
Degree
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
This Specialization has ACE® recommendation. It is eligible for college credit at participating U.S. colleges and universities. Note: The decision to accept specific credit recommendations is up to each institution.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
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The specialization requires 36-48 hours of effort to complete. Working 10-12 hours a week, it can be completed within 1-2 months. Working 2-3 hours a week it can be completed in 4-6 months.
Just basic computer literacy and willingness to self-learn online. No prior knowledge of computer science or programming languages required.
It is recommended that you complete the first 2 courses in order before the remaining courses.
Yes. The IBM Data Science Fundamentals with Python and SQL Specialization recently secured a credit recommendation from the American Council on Education's (ACE) Credit Recommendation, which is the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 8 college credits for completing the program. This aims to help open up additional pathways to learners who are interested in higher education and prepare them for entry-level jobs.
Upon successfully completing these courses, you will have the practical knowledge and experience to take start tackling Data Science problems and challenges.
Sí, se recomienda encarecidamente realizar los cursos en el orden sugerido, ya que se basan en conceptos de los cursos anteriores.
No hay créditos universitarios asociados a esta especialización en este momento.
To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credlybadge, which contains the ACE®️credit recommendation. Once claimed, you will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.
Please see Coursera’s ACE Recommendations FAQ.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
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